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03/10/2023
Ignorance is Bliss: Robust Control via Information Gating
Informational parsimony – i.e., using the minimal information required f...
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12/29/2022
Offline Policy Optimization in RL with Variance Regularizaton
Learning policies from fixed offline datasets is a key challenge to scal...
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12/28/2022
Representation Learning in Deep RL via Discrete Information Bottleneck
Several self-supervised representation learning methods have been propos...
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11/02/2022
Behavior Prior Representation learning for Offline Reinforcement Learning
Offline reinforcement learning (RL) struggles in environments with rich ...
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11/01/2022
Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning
Goal-conditioned reinforcement learning (RL) is a promising direction fo...
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10/31/2022
Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information
Learning to control an agent from data collected offline in a rich pixel...
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07/17/2022
Guaranteed Discovery of Controllable Latent States with Multi-Step Inverse Models
A person walking along a city street who tries to model all aspects of t...
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12/31/2021
Importance of Empirical Sample Complexity Analysis for Offline Reinforcement Learning
We hypothesize that empirically studying the sample complexity of offlin...
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12/11/2019
Marginalized State Distribution Entropy Regularization in Policy Optimization
Entropy regularization is used to get improved optimization performance ...
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12/11/2019
Doubly Robust Off-Policy Actor-Critic Algorithms for Reinforcement Learning
We study the problem of off-policy critic evaluation in several variants...
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12/11/2019
Entropy Regularization with Discounted Future State Distribution in Policy Gradient Methods
The policy gradient theorem is defined based on an objective with respec...
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11/16/2019
Off-Policy Policy Gradient Algorithms by Constraining the State Distribution Shift
Off-policy deep reinforcement learning (RL) algorithms are incapable of ...
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06/09/2019
Transfer Learning by Modeling a Distribution over Policies
Exploration and adaptation to new tasks in a transfer learning setup is ...
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01/30/2019
InfoBot: Transfer and Exploration via the Information Bottleneck
A central challenge in reinforcement learning is discovering effective p...
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11/30/2018
An Introduction to Deep Reinforcement Learning
Deep reinforcement learning is the combination of reinforcement learning...
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11/27/2018
Prioritizing Starting States for Reinforcement Learning
Online, off-policy reinforcement learning algorithms are able to use an ...
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07/11/2018
VFunc: a Deep Generative Model for Functions
We introduce a deep generative model for functions. Our model provides a...
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11/12/2017
Alpha-Divergences in Variational Dropout
We investigate the use of alternative divergences to Kullback-Leibler (K...
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10/13/2017
Bayesian Hypernetworks
We propose Bayesian hypernetworks: a framework for approximate Bayesian ...
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09/19/2017
Deep Reinforcement Learning that Matters
In recent years, significant progress has been made in solving challengi...
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03/08/2017